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JCR Impact Factor: 0.595
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Issues per year: 4
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Next issue: Feb 2018
Avg review time: 107 days


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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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LATEST NEWS

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

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  4/2017 - 16
View TOC | « Previous Article | Next Article »

Ubiquity of Wi-Fi: Crowdsensing Properties for Urban Fingerprint Positioning

LECA, C. L. See more information about LECA, C. L. on SCOPUS See more information about LECA, C. L. on IEEExplore See more information about LECA, C. L. on Web of Science
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,316 KB) | Citation | Downloads: 79 | Views: 117

Author keywords
crowdsourcing, ubiquitous computing, wireless sensor networks, wireless LAN, data collection

References keywords
indoor(13), positioning(8), wifi(7), location(7), localization(7), systems(5), signal(5), networks(5), mobile(5), wlan(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-11-30
Volume 17, Issue 4, Year 2017, On page(s): 131 - 136
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.04016
Web of Science Accession Number: 000417674300016
SCOPUS ID: 85035800656

Abstract
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Positioning systems based on location fingerprinting have become an area of intense research, mainly with the aim of providing indoor localization. Many challenges arise when trying to deploy location fingerprinting to an outdoor environment. The main problem is achieving coverage of large outdoor spaces, which needs an intensive data gathering effort. This paper proposes the use of mobile crowdsensing in order to build a fingerprint database consisting of Wi-Fi networks received signal strength measurements. Mobile crowdsensing is represented by the usage of smart-phones equipped with GPS and Wi-Fi sensors for the collection of fingerprints. The primary objective of this work is to prove the feasibility of urban positioning using Wi-Fi crowdsensed data by showing that Wi-Fi networks are ubiquitous in urban areas. We then examine the gathered data and report our findings on challenges in building and maintaining a large-scale fingerprint database, the influence of the data collection method on the Wi-Fi data and the influence of fading on measurements. As Wi-Fi access-points are shown to exhibit mobility, we also propose and analyze methods for detecting and classification of mobile and static access-points.


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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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